This paper investigates the role of firm-level investments in specific digital technologies (DTs) in shaping the propensity to introduce circular economy innovations (CEIs), and on the intensity of CEIs. We further differentiate CEIs according to the core principles of the circular economy, namely, reduction, recycling, and reuse (3R). Moving beyond treating digitalization as a homogeneous aggregate, we apply multiple correspondence analysis (MCA) to firm-level adoption patterns and identify a core bundle of jointly adopted technologies comprising the Internet of Things, Big Data, Cloud Computing, 3D Printing, and Cybersecurity. Firms adopting this bundle exhibit stronger CEI outcomes than those adopting other technologies in isolation, indicating that complementarities among DTs amplify their enabling role. Industrial robotics retains an independent positive effect, whereas artificial intelligence shows no significant association. We further document that the enabling effect of DT on CEI is more pronounced in urban and intermediate regions, while it holds across both high- and low-GHG-emission industries. Our findings remain robust across a range of additional specifications and tests addressing potential endogeneity concerns.

Digital Technologies and Circular Economy Innovations: Evidence from Manufacturing Firms in Emilia-Romagna and Veneto

Roberto Antonietti
Conceptualization
;
2026

Abstract

This paper investigates the role of firm-level investments in specific digital technologies (DTs) in shaping the propensity to introduce circular economy innovations (CEIs), and on the intensity of CEIs. We further differentiate CEIs according to the core principles of the circular economy, namely, reduction, recycling, and reuse (3R). Moving beyond treating digitalization as a homogeneous aggregate, we apply multiple correspondence analysis (MCA) to firm-level adoption patterns and identify a core bundle of jointly adopted technologies comprising the Internet of Things, Big Data, Cloud Computing, 3D Printing, and Cybersecurity. Firms adopting this bundle exhibit stronger CEI outcomes than those adopting other technologies in isolation, indicating that complementarities among DTs amplify their enabling role. Industrial robotics retains an independent positive effect, whereas artificial intelligence shows no significant association. We further document that the enabling effect of DT on CEI is more pronounced in urban and intermediate regions, while it holds across both high- and low-GHG-emission industries. Our findings remain robust across a range of additional specifications and tests addressing potential endogeneity concerns.
2026
   Growing Resilient, INclusive and Sustainable
   GRINS
   European Union - PNRR
   GRINS PE00000018 – CUP C93C22005270001
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3616722
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